Introduzione a R

class: title-slide, center, middle

#.title[Introduzione a R]

#.subtitle[R and beyond]

###.location[ARCA - @DPSS]

###.author[Filipo Gambarota]

---
class: section, center, middle

# What to do now? 🤔

---
# Cosa abbiamo visto?

- I tipi di oggetti in R
- Strutture dati: come creare, accedere, manipolare
- Programmazione condizionale e iterativa (`for`, `if else`, `*apply`)
- Importare/esportare file

Un sacco di cose! 😄. Ma è tutto qui? Nope 🤪

---
# Manipolazione dataframe avanzata con `dplyr`

Possiamo utilizzare tutte le conoscenze su come manipolare i dataframe e sulle tipologie di dato con un pacchetto che ormai è diventato lo standard. `dplyr` permette di fare qualsiasi cosa con un dataframe:

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/dplyr.svg" alt="&lt;a href=&quot;https://dplyr.tidyverse.org/&quot;&gt;https://dplyr.tidyverse.org/&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://dplyr.tidyverse.org/">https://dplyr.tidyverse.org/</a></p>
</div>

]

.pull-right[

- ordinare righe e colonne
- modificare colonne con operazioni complesse
- selezionare righe e colonne con operazioni complesse

]

---
# Trasformare dataframe con `tidyr`

Spesso dobbiamo trasformare i dataframe in diversi formati ad esempio da formato `long` a formato `wide` e viceversa. Questa operazione è molto complessa ma `tiyr` la implementa in modo estremamente semplice. Creiamo un dataframe in formato `long`:

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/tidyr.svg" alt="&lt;a href=&quot;https://tidyr.tidyverse.org/&quot;&gt;https://tidyr.tidyverse.org/&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://tidyr.tidyverse.org/">https://tidyr.tidyverse.org/</a></p>
</div>

]

.pull-right[

```r
datlong <- expand.grid(
    id = 1:10,
    cond = c("a", "b", "c")
)

datlong$y <- rnorm(nrow(datlong))

head(datlong)
```

```
##   id cond          y
## 1  1    a  0.1692748
## 2  2    a  0.7628282
## 3  3    a -1.3338512
## 4  4    a -0.4735758
## 5  5    a  0.6818886
## 6  6    a  0.3342488
```

]

---
# Trasformare dataframe con `tidyr`

Adesso possiamo trasformare il dataframe in formato `wide` con una semplice linea di codice con la funzione `pivot_wider()`

```r
datwide <- tidyr::pivot_wider(datlong, names_from = cond, values_from = y)

head(datlong)
```

```
##   id cond          y
## 1  1    a  0.1692748
## 2  2    a  0.7628282
## 3  3    a -1.3338512
## 4  4    a -0.4735758
## 5  5    a  0.6818886
## 6  6    a  0.3342488
```

---
# Trasformare dataframe con `tidyr`

Chiaramente possiamo anche ritornare al formato originale usando la funzione `pivot_longer()`:

```r
datlong <- tidyr::pivot_longer(datwide, c(a, b, c), names_to = "cond", values_to = "y")

head(datlong)
```

```
## [90m# A tibble: 6 x 3[39m
##      id cond       y
##   [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m  [3m[90m<dbl>[39m[23m
## [90m1[39m     1 a      0.169
## [90m2[39m     1 b     -[31m0[39m[31m.[39m[31m645[39m
## [90m3[39m     1 c     -[31m1[39m[31m.[39m[31m15[39m 
## [90m4[39m     2 a      0.763
## [90m5[39m     2 b     -[31m0[39m[31m.[39m[31m783[39m
## [90m6[39m     2 c      0.507
```

---
# Combinare funzioni con `%>%` o `|>`

Quando vi capitera di cercare codice o soluzioni online che riguardano R, il 90% del codice avrà questi strani simboli `%>%` o `|>`. Abbiamo già imparato che questi sono operatori (e quindi funzioni). Questi operatori permettono di concatenare una serie di funzioni in modo molto intuitivo e leggibile. In altri termini `f(x)` è equivalente a `x %>% f()`. Queste si chiamano **pipes**:

```r
x <- rnorm(100)
mean(x)
```

```
## [1] 0.1950063
```

```r
x %>% mean()
```

```
## [1] 0.1950063
```

```r
x |> mean()
```

```
## [1] 0.1950063
```

---
# Combinare funzioni con `%>%` o `|>`

Con funzioni semplici non hanno molto senso ma con *pipeline* più complesse il codice diventa molto leggibile. Se volessimo concatenare una serie di funzioni di `dplyr` (ma funziona con tutte le altre funzioni):

```r
iris <- group_by(iris, Species)
iris <- summarise(iris, y = mean(Sepal.Width))
iris <- arrange(iris, y)
iris <- mutate(iris, z = y + 1)
iris <- filter(iris, y > 0)
```

Usando la **pipe**:

```r
iris %>%
  group_by(Species) %>%
  summarise(y = mean(Sepal.Width)) %>%
  arrange(y) %>%
  mutate(z = y + 1) %>%
  filter(y > 0)
```

---
# Grafici super con `ggplot2`

Un pacchetto estremamente potente che si basa su molte delle cose che abbiamo visto e sulla sintassi simil `dplyr` e **pipe-based** è `ggplot2`:

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/ggplot2.svg" alt="&lt;a href=&quot;https://ggplot2.tidyverse.org/index.html&quot;&gt;https://ggplot2.tidyverse.org/index.html&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://ggplot2.tidyverse.org/index.html">https://ggplot2.tidyverse.org/index.html</a></p>
</div>

]

.pull-right[

```r
ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + 
    geom_point(size = 3) +
    theme_minimal(base_size = 20) +
    theme(legend.position = "bottom")
```

]

---
# Programmazione funzionale/iterativa

Il pacchetto `purrr` fornisce un insieme di funzioni che vanno a migliorare la `*apply` family con funzioni extra ed una maggiore robustezza. Se avete capito l'`*apply` family, `purrr` può essere un modo più efficiente di utilizzarle

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/purrr.svg" alt="&lt;a href=&quot;https://tidyr.tidyverse.org/&quot;&gt;https://tidyr.tidyverse.org/&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://tidyr.tidyverse.org/">https://tidyr.tidyverse.org/</a></p>
</div>

]

.pull-right[

```r
sapply(mtcars, mean)
```

```
##        mpg        cyl       disp         hp       drat         wt       qsec         vs         am 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750   0.437500   0.406250 
##       gear       carb 
##   3.687500   2.812500
```

```r
map_dbl(mtcars, mean) # controlla che l'output sia sempre numerico
```

]

---
# The amazing `tidyverse`

Tutto questo (e molto altro) fa parte di un insieme di pacchetti per lavorare con i dati raccolti in un meta-pacchetto chiamato `tidyverse`. Questo permette di lavorare in molto molto più consistente ed intuitivo per manipolare, analizzare e rappresentare dati di tutti i tipi.

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/tidyverse.svg" alt="&lt;a href=&quot;https://www.tidyverse.org/&quot;&gt;https://www.tidyverse.org/&lt;/a&gt;" width="20%" />
<p class="caption"><a href="https://www.tidyverse.org/">https://www.tidyverse.org/</a></p>
</div>

---
# Come approfondire?

Adesso abbiamo le competenze di base per capire qualsiasi nuova funzione/pacchetto in R. Per approfondire il `tidyverse` vi consiglio:

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/r4ds.png" alt="&lt;a href=&quot;https://r4ds.had.co.nz/&quot;&gt;https://r4ds.had.co.nz/&lt;/a&gt;" width="20%" />
<p class="caption"><a href="https://r4ds.had.co.nz/">https://r4ds.had.co.nz/</a></p>
</div>

---
# Come approfondire?

Per capire a fondo tutte le cose che abbiamo visto ed espandere al massimo la conoscenza di R come linguaggio di programmazione vi consiglio il libro **advanced R**

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/advancedR.jpg" alt="&lt;a href=&quot;https://adv-r.hadley.nz/&quot;&gt;https://adv-r.hadley.nz/&lt;/a&gt;" width="20%" />
<p class="caption"><a href="https://adv-r.hadley.nz/">https://adv-r.hadley.nz/</a></p>
</div>

---
# Come approfondire?

Anche `ggplot2` ha un suo libro associato che oltre a fornire un framework teorico sulle rappresentazioni grafiche in generale chiamato *grammar of graphics* spiega nel dettaglio il funzionamento del pacchetto

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/ggplotbook.jpg" alt="&lt;a href=&quot;https://ggplot2-book.org/&quot;&gt;https://ggplot2-book.org/&lt;/a&gt;" width="20%" />
<p class="caption"><a href="https://ggplot2-book.org/">https://ggplot2-book.org/</a></p>
</div>

---
class: section, center, middle

# Altri argomenti utili...

---
# Lavorare in modo avanzato con le stringhe

Le stringhe sono uno strumento molto potente da conoscere per organizzare al meglio le vostre analisi. E' anche un argomento vasto e complesso ma questi riferimenti sono un buon punto di partenza:

- [Capitolo 16](https://psicostat.github.io/Introduction2R/stringhe.html) del libro `introduction2R`
- [Capitolo 14](https://r4ds.had.co.nz/strings.html) del libro `R4DS`
- [Regex](https://cran.r-project.org/web/packages/stringr/vignettes/regular-expressions.html) ovvero Regular Expressions

---
# Literate programming - R Markdown

R Markdown è uno strumento estramente potente che permette di combinare testo e codice per creare documenti, slides (queste ad esempio), siti web, tesi, articoli scientifici etc.

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/knitr.svg" alt="&lt;a href=&quot;https://cran.r-project.org/web/packages/knitr/index.html&quot;&gt;knitr website&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://cran.r-project.org/web/packages/knitr/index.html">knitr website</a></p>
</div>

]

.pull-right[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/rmarkdown.svg" alt="&lt;a href=&quot;https://rmarkdown.rstudio.com/&quot;&gt;https://rmarkdown.rstudio.com/&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://rmarkdown.rstudio.com/">https://rmarkdown.rstudio.com/</a></p>
</div>

]

---
# Creare tabelle

Spesso dobbiamo creare delle tabelle da inserire nei documenti o in file R Markdown. Ci sono diversi pacchetti per questo ad esempio `flextable()`, `kableExtra()` o anche `sjPlot()`.

Creiamo una tabella di statistiche descrittive:

```r
df_sum <- iris |> 
    group_by(Species) |> 
    summarise(mean = mean(Sepal.Length),
              sd = sd(Sepal.Length),
              se = sd / sqrt(n()),
              min = min(Sepal.Length),
              max = max(Sepal.Length))
df_sum
```

```
## [90m# A tibble: 3 x 6[39m
##   Species     mean    sd     se   min   max
##   [3m[90m<fct>[39m[23m      [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m  [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m
## [90m1[39m setosa      5.01 0.352 0.049[4m8[24m   4.3   5.8
## [90m2[39m versicolor  5.94 0.516 0.073[4m0[24m   4.9   7  
## [90m3[39m virginica   6.59 0.636 0.089[4m9[24m   4.9   7.9
```

---
# Creare tabelle con `flextable`

`flextable` permette di creare tabelle molto complesse e di salvarle in vari formati tra cui `docx` per poterle usare poi in formato `word`. Il punto di partenza è sempre un dataframe. Il sito di [`flextable`](https://ardata-fr.github.io/flextable-book/) contiene tantissa documentazione.

```r
df_sum |>
    flextable() |> 
    theme_vanilla() |> 
    autofit() |> 
    colformat_double(digits = 2)
```

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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-b2342084{width:1.092in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0.5pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-b2342085{width:0.557in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0.5pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-b2342086{width:0.591in;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0.5pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table class='cl-b23ca57e'><thead><tr style="overflow-wrap:break-word;"><td class="cl-b2342034"><p class="cl-b233ed9e"><span class="cl-b22e3b6a">Species</span></p></td><td class="cl-b234203e"><p class="cl-b233eda8"><span class="cl-b22e3b6a">mean</span></p></td><td class="cl-b234203f"><p class="cl-b233eda8"><span class="cl-b22e3b6a">sd</span></p></td><td class="cl-b2342048"><p class="cl-b233eda8"><span class="cl-b22e3b6a">se</span></p></td><td class="cl-b2342049"><p class="cl-b233eda8"><span class="cl-b22e3b6a">min</span></p></td><td class="cl-b234204a"><p class="cl-b233eda8"><span class="cl-b22e3b6a">max</span></p></td></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-b2342052"><p class="cl-b233ed9e"><span class="cl-b22e3b74">setosa</span></p></td><td class="cl-b2342053"><p class="cl-b233eda8"><span class="cl-b22e3b74">5.01</span></p></td><td class="cl-b234205c"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.35</span></p></td><td class="cl-b234205d"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.05</span></p></td><td class="cl-b234205e"><p class="cl-b233eda8"><span class="cl-b22e3b74">4.30</span></p></td><td class="cl-b2342066"><p class="cl-b233eda8"><span class="cl-b22e3b74">5.80</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-b2342067"><p class="cl-b233ed9e"><span class="cl-b22e3b74">versicolor</span></p></td><td class="cl-b2342068"><p class="cl-b233eda8"><span class="cl-b22e3b74">5.94</span></p></td><td class="cl-b2342070"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.52</span></p></td><td class="cl-b2342071"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.07</span></p></td><td class="cl-b2342072"><p class="cl-b233eda8"><span class="cl-b22e3b74">4.90</span></p></td><td class="cl-b2342073"><p class="cl-b233eda8"><span class="cl-b22e3b74">7.00</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-b234207a"><p class="cl-b233ed9e"><span class="cl-b22e3b74">virginica</span></p></td><td class="cl-b234207b"><p class="cl-b233eda8"><span class="cl-b22e3b74">6.59</span></p></td><td class="cl-b234207c"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.64</span></p></td><td class="cl-b2342084"><p class="cl-b233eda8"><span class="cl-b22e3b74">0.09</span></p></td><td class="cl-b2342085"><p class="cl-b233eda8"><span class="cl-b22e3b74">4.90</span></p></td><td class="cl-b2342086"><p class="cl-b233eda8"><span class="cl-b22e3b74">7.90</span></p></td></tr></tbody></table></div></template>
<div class="flextable-shadow-host" id="d5570d06-d97b-4b74-b967-8b7223d504e7"></div>
<script>
var dest = document.getElementById("d5570d06-d97b-4b74-b967-8b7223d504e7");
var template = document.getElementById("4766fdc1-2f36-4225-b4ef-69d690d61897");
var caption = template.content.querySelector("caption");
var fantome = dest.attachShadow({mode: 'open'});
var templateContent = template.content;
fantome.appendChild(templateContent);
</script>

---
# Creare tabelle con `kableExtra`

`kableExtra` è un altro ottimo pacchetto molto utilizzato per documenti R Markdown per creare tabelle in **html** e **pdf**. La documentazione per tabelle in [**html**](https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html) e [**pdf**](https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_pdf.pdf) è ottima. Si parte sempre da un dataframe:

```r
df_sum |>
    kable() |> 
    kable_styling(bootstrap_options = c("striped"), 
                  full_width = FALSE)
```

<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Species </th>
   <th style="text-align:right;"> mean </th>
   <th style="text-align:right;"> sd </th>
   <th style="text-align:right;"> se </th>
   <th style="text-align:right;"> min </th>
   <th style="text-align:right;"> max </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> setosa </td>
   <td style="text-align:right;"> 5.006 </td>
   <td style="text-align:right;"> 0.3524897 </td>
   <td style="text-align:right;"> 0.0498496 </td>
   <td style="text-align:right;"> 4.3 </td>
   <td style="text-align:right;"> 5.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> versicolor </td>
   <td style="text-align:right;"> 5.936 </td>
   <td style="text-align:right;"> 0.5161711 </td>
   <td style="text-align:right;"> 0.0729976 </td>
   <td style="text-align:right;"> 4.9 </td>
   <td style="text-align:right;"> 7.0 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> virginica </td>
   <td style="text-align:right;"> 6.588 </td>
   <td style="text-align:right;"> 0.6358796 </td>
   <td style="text-align:right;"> 0.0899270 </td>
   <td style="text-align:right;"> 4.9 </td>
   <td style="text-align:right;"> 7.9 </td>
  </tr>
</tbody>
</table>

---
# Creare tabelle da modelli statistici con `sjPlot`

[`sjPlot`](https://cran.r-project.org/web/packages/sjPlot/index.html) è un pacchetto tuttofare che tra le altre cose crea grafici e tabelle partendo non da dataframe ma da oggetti di modelli inserendo una serie di informazioni utili in automatico.

```r
fit <- lm(Sepal.Length ~ Petal.Length, 
          data = iris)

sjPlot::tab_model(fit)
```

<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">&nbsp;</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">Sepal.Length</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">Predictors</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">Estimates</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">CI</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">p</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">4.31</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">4.15&nbsp;&ndash;&nbsp;4.46</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">Petal Length</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.41</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.37&nbsp;&ndash;&nbsp;0.45</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">Observations</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">150</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">R<sup>2</sup> / R<sup>2</sup> adjusted</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">0.760 / 0.758</td>
</tr>

</table>

---
# Creare tabelle da modelli statistici con `broom`

[`broom`](https://cran.r-project.org/web/packages/broom/vignettes/broom.html) ha un approccio diverso (quello che preferisco) creando dei dataframe da modelli statistici e poi lasciando all'utente la creazione della tabella con `flextable` o `kableExtra`:

.pull-left[

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#img/broom.svg" alt="&lt;a href=&quot;https://broom.tidymodels.org&quot;&gt;https://broom.tidymodels.org&lt;/a&gt;" width="50%" />
<p class="caption"><a href="https://broom.tidymodels.org">https://broom.tidymodels.org</a></p>
</div>

]

.pull-rigth[

```r
tidfit <- broom::tidy(fit)
tidfit
```

```
## [90m# A tibble: 2 x 5[39m
##   term         estimate std.error statistic   p.value
##   [3m[90m<chr>[39m[23m           [3m[90m<dbl>[39m[23m     [3m[90m<dbl>[39m[23m     [3m[90m<dbl>[39m[23m     [3m[90m<dbl>[39m[23m
## [90m1[39m (Intercept)     4.31     0.078[4m4[24m      54.9 2.43[90me[39m[31m-100[39m
## [90m2[39m Petal.Length    0.409    0.018[4m9[24m      21.6 1.04[90me[39m[31m- 47[39m
```

]

---
# Materiale

Nel sito del corso [arca-dpss.github.io/course-R](https://arca-dpss.github.io/course-R/) è presente una sezione con articoli, libri e materiale di approfondimento di tutto quello che abbiamo visto e di questi argomenti extra. Nel tempo questo materiale potrebbe crescere ma sarà sempre disponibile nel link indicato 😄